Exchangeability of measures of association before and after exposure status is flipped: its relationship with confounding in the counterfactual model. Etsuji Suzuki, Michio Yamamoto, Eiji Yamamoto. Journal of Epidemiology. https://t.co/elI5r9TFFg
So happy to share our paper just published in Journal of Epidemiology assessing bias of odds ratio estimators from logistic regression methods with sparse data sets!
https://t.co/7VYasZRD9j https://t.co/teS0Hr3RGo
Ameronothrus twitter sp. nov. (Acari, Oribatida) a New Coastal Species of Oribatid Mite from Japan. Tobias Pfingstl, Shimpei F. Hiruta, Takamasa Nemoto, Wataru Hagino, Satoshi Shimano. Species Diversity. https://t.co/ZOuT3PRfVY
Cochran WG. Some Methods for Strengthening the Common χ2 Tests. Biometrics 1954. https://t.co/HuMnZogU4O
岩崎学. mid-P value:その考え方と特性. 応用統計学 1993 . https://t.co/ceaxylsbYg
#J_Epidemi 2023 June Issue:
Publisher Correction: “Bias in odds ratios from logistic regression methods with sparse data sets” [J Epidemiol 33(6) (2023) 265-275]
The correct article type is “Review Article”. We apologizes for this error.
https://t.co/o4FJhZYA8W
@J_Epidemi https://t.co/iWOx7SVcat
DPCのCardiovascular Diseases のバリデーションスタディです。
Validity of Diagnostic Algorithms for Cardiovascular Diseases in Japanese Health Insurance Claims (Circ J 2023; 87: 536–542)
https://t.co/IOaJaPQ0oI
I am pleased to share our new tutorial paper on propensity score methods. We compared identifiably assumptions, modeling decisions, and causal estimand of the alternative PS-based methods and multivariable outcome regression.
https://t.co/B99dL38vsR
Our new pedagogic paper with a fresh numerical example for longitudinal causal inference is published as a part of “Pitfalls and Tips” series in Journal of Epidemiology
Understanding marginal structural models for time-varying exposures: pitfalls and tips https://t.co/UNX3L7l5VP
1st piece of new methodological review series in Journal of Epidemiology. Rather than providing introductory tutorial for causal DAGs, we reviewed technical difficulties often overlooked when learning them.
Suzuki et al. Causal diagrams: pitfalls and tips https://t.co/RRcyD2Yd5A